For this approach, we employ the Faster-RCNN model for visual object detection [1]} (see sec:back:cnn-based-detectors:two-stages-detectors for further details);
\(f_\theta \)  produces a list of bounding boxes following the standard two-stage detection paradigm.
In the first step, a Region Proposal Network (RPN) generates
the region proposals that might contain objects, slicing pre-defined region boxes (called anchors);
in the second step, these priors are refined, performing a regression to the coordinates of bounding boxes precisely localizing the objects inside these Regions of Interest (RoIs).
The centers of the boxes comprise the final localization of the entities.
Targets are produced by generating squared bounding boxes centered in the dot-annotated data with a fixed side, again, depending on the typical object size in the dataset.
We implement \(f_\theta \)  as a Faster-RCNN network with a Feature Pyramid Network module and a ResNet-50 backbone. From now on, we will refer to this method as FRCNN.
